A new genomic resource to enable standardized surveys of <scp>SNPs</scp> across the native range of brook trout (<i>Salvelinus fontinalis</i>)
Bibliographic record
Abstract
Understanding how genetic diversity is distributed across spatiotemporal scales in species of conservation or management concern is critical for identifying large-scale mechanisms affecting local conservation status and implementing large-scale biodiversity monitoring programmes. However, cross-scale surveys of genetic diversity are often impractical within single studies, and combining datasets to increase spatiotemporal coverage is frequently impeded by using different sets of molecular markers. Recently developed molecular tools make surveys based on standardized single-nucleotide polymorphism (SNP) panels more feasible than ever, but require existing genomic information. Here, we conduct the first survey of genome-wide SNPs across the native range of brook trout (Salvelinus fontinalis), a cold-adapted species that has been the focus of considerable conservation and management effort across eastern North America. Our dataset can be leveraged to easily design SNP panels that allow datasets to be combined for large-scale analyses. We performed restriction site-associated DNA sequencing for wild brook trout from 82 locations spanning much of the native range and domestic brook trout from 24 hatchery strains used in stocking efforts. We identified over 24,000 SNPs distributed throughout the brook trout genome. We explored the ability of these SNPs to resolve relationships across spatial scales, including population structure and hatchery admixture. Our dataset captures a wide spectrum of genetic diversity in native brook trout, offering a valuable resource for developing SNP panels. We highlight potential applications of this resource with the goal of increasing the integration of genomic information into decision-making for brook trout and other species of conservation or management concern.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.006 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.007 | 0.005 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".